Many modern day industries are beginning to rely more and more on robotic manipulators such as robotic arms. These robotic manipulators may function to increase repeatability of tasks, increase efficiency of production lines, and bring other benefits to their operators. Conventionally, robotic manipulators may be trained to grasp and move objects through manual operation by human operators. Some training may also be performed by ingesting data describing how similar robotic manipulators successfully grasped different objects.
Under operational conditions, a robotic manipulator identifies an object and relies on its earlier learning to select an appropriate grasp for grasping and moving the object. This typically may include decomposing the object to one of a set of geometric primitive shapes (e.g., squares, rectangles, cylinders, etc.) and selecting a trained grasp associated with one of the primitives. This approach is sometimes referred to as an affordance-based approach. Depending on the complexity and orientation of the object, this approach can be resource and time intensive. For example, the robotic manipulator (e.g., a computer that manages the robotic manipulator) may be required to evaluate a large set of possible grasps to identify the appropriate grasp. In addition, this approach focuses primarily on the appropriate grasp, which may or may not consider downstream processes relating to the object (e.g., grasping and movement by a second robotic manipulator at a later time). Because of these reasons, other systems that rely on the object being removed may be impacted and the benefits of the robotic manipulator may be minimized.